A Survey of Unsupervised Domain Adaptation for Visual Recognition

Understand

While huge volumes of unlabeled data are generated and made available in many domains, the demand for automated understanding of visual data is higher than ever before.

• Most existing machine learning models typically rely on massive amounts of labeled training data to achieve high performance.
• Unfortunately, such a requirement cannot be met in real-world applications.
• The number of labels is limited and manually annotating data is expensive and time-consuming.